MAREANO - NiN - loDens <1031.8 without SpitsbergenBanken outliers
Last Rendered on 30 september, 2022
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layoutlibrary(purrr)
library(furrr)Loading required package: futuresource("gg_ordisurf_viridis.R")LOAD OBJECTS IF HAVE RUN ALREADY
geodist_pa<-readRDS(file.path(dataPath,"inputs/BarentsDeep_geodist_pa.rds"))
geodist_r6<-readRDS(file.path(dataPath,"inputs/BarentsDeep_geodist_r6.rds"))
mds_pa<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_pa.rds"))
mds_r6<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_r6.rds"))
#mds_r6_1000<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_r6_1000rep.rds"))
ep<-0.8 #epsilon (to avoid having to find and run just this line from code blocks where the mds objects were made)Combine Barents and Deep datasets
env_sort1 <- read.csv(file.path(dataPath,"inputs/SPLITS/BARENTS/LDnoSBhi02_env_sort_2022-09-29.csv")) %>% as.data.frame
otu_sort1 <- read.csv(file.path(dataPath,"inputs/SPLITS/BARENTS/LDnoSBhi02_otu_sort_2022-09-29.csv")) %>% as.data.frame
env_sort2 <- read.csv(file.path(dataPath,"inputs/SPLITS/DEEP/HDensDeep_env_sort_2022-09-29.csv")) %>% as.data.frame
otu_sort2 <- read.csv(file.path(dataPath,"inputs/SPLITS/DEEP/HDensDeep_otu_sort_2022-09-29.csv")) %>% as.data.frame
env_orig <- rbind(env_sort1,env_sort2)
otu_orig <- rbind(otu_sort1, otu_sort2)#joinedDat<-otu_orig%>% dplyr::left_join(.,env_orig, by="SampID")
joinedDat<- left_join(otu_orig,env_orig, by=c("SampID"="SampID"))
otu_sort<-joinedDat %>% select(c(2:391))
env_sort<-joinedDat %>% select(c(2,393:528))
# Sorting and ordering by SampID
# env_orig <- env_orig[order(env_orig$SampID), ]
# otu_orig <- otu_orig[order(otu_orig$SampID), ]
table(env_sort$SampID == otu_sort$SampID)
TRUE
1883 #must be same length and equal to unique sample number length - the ordered lists are assumed to be directly relatable on a row by row basis
dim(env_sort)[1] 1883 137dim(otu_sort)[1] 1883 390Data
table(env_sort$SampID2 == otu_sort$SampID)< table of extent 0 >#must be same length and equal to unique sample number length - the ordered lists are assumed to be directly relatable on a row by row basis
dim(env_sort)[1] 1883 137dim(otu_sort)[1] 1883 390remove any variables with not enough coverage
env_sort <- env_sort %>% select(-c("BO22_lightbotmean_bdmean",
"BO22_lightbotltmax_bdmean",
"BO22_lightbotltmin_bdmean",
"BO22_lightbotrange_bdmean"))
env_sort$X.y<-env_sort$X.y.yPlot map to check location of samples
env_sort_locations<- ggplot(data = env_sort,
aes(x = X.y,
y = Y)) +
theme_classic() +
geom_point(aes(colour = bathy),
size = 2) +
scale_colour_gradient2(low = "red",
mid = "yellow",
high = "green") +
ggtitle("Location of samples in sorted env file")
env_sort_locations
Data cleaning
## Removing NAs
otuCompl <- otu_sort[complete.cases(env_sort[, -c(1:which(colnames(env_sort)=="bathy")-1)]), ]
envCompl <- env_sort[complete.cases(env_sort[, -c(1:which(colnames(env_sort)=="bathy")-1)]), ]
## Removing observations with less than 4 OTUs
sel <- rowSums(otuCompl[, -c(1)]) >= 4
otuSel <- otuCompl[sel, ]
envSel <- envCompl[sel, ]
## Removing phosphate
#envSel <- envSel %>% select(-phosphate_mean.tif)
dim(otuSel); dim(envSel)[1] 1676 390
[1] 1676 134Show what samples are left after complete cases and >4 OTUs filters
env_sel_locations<- ggplot(data = envSel,
aes(x = X.y,
y = Y)) +
theme_classic() +
geom_point(aes(colour = bathy),
size = 2) +
scale_colour_gradient2(low = "red",
mid = "yellow",
high = "green") +
ggtitle("Location of samples in sorted env file")
env_sel_locations
Show what got removed in complete cases filter
envCompl_ccrem<-env_sort%>%filter(!SampID%in%envCompl$SampID)
envCompl_ccrem_locations<- ggplot(data = envCompl_ccrem,
aes(x = X.y,
y = Y)) +
theme_classic() +
geom_point(aes(colour = bathy),
size = 2) +
scale_colour_gradient2(low = "red",
mid = "yellow",
high = "green") +
ggtitle("Location of removed complete case samples resulting in envSel file")
envCompl_ccrem_locations
Show what got removed in <4 OTUs filter
inv.sel <- rowSums(otuCompl[, -c(1:2)]) < 4
env.invSel <- envCompl[inv.sel, ]
env.invSel_locations<- ggplot(data = env.invSel,
aes(x = X.y,
y = Y)) +
theme_classic() +
geom_point(aes(colour = bathy),
size = 2) +
scale_colour_gradient2(low = "red",
mid = "yellow",
high = "green") +
ggtitle("Location of removed samples with <4 OTUs resulting in envSel file")
env.invSel_locations
[Optional] Data thinning
# otu_red <- otu1[1:500, ]
# otu <- otu_red
#
# env_red <- env[1:500, ]
# env <- env_redWhole dataset for first run
otu<-otuSel[,-c(1:which(colnames(otuSel)=="Acesta_excavata")-1)]
env<-envSel[, -c(2:(which(colnames(envSel)=="bathy")-1))]
table(is.na(otu))
FALSE
651964 table(is.na(env))
FALSE
197768 #str(otuSel)
#str(envSel)Splitting in subsets
# ## Class 1
# otu1 <- subset(otuSel, envSel$SplitRev == 1)
# env1 <- envSel %>% filter(SplitRev == 1)
#
# ## Class 2
# otu2 <- subset(otuSel, envSel$SplitRev == 2)
# env2 <- envSel %>% filter(SplitRev == 2)
#
# ## Class 4
# otu3 <- subset(otuSel, envSel$SplitRev == 3)
# env3 <- envSel %>% filter(SplitRev == 3)
#
# ## Class 4
# otu4 <- subset(otuSel, envSel$SplitRev == 4)
# env4 <- envSel %>% filter(SplitRev == 4)
#
# ## Class 6
# otu6 <- subset(otuSel, envSel$SplitRev == 6)
# env6 <- envSel %>% filter(SplitRev == 6)
#
# ## Class 7
# otu7 <- subset(otuSel, envSel$SplitRev == 7)
# env7 <- envSel %>% filter(SplitRev == 7)
#
# ## Class 8
# otu8 <- subset(otuSel, envSel$SplitRev == 8)
# env8 <- envSel %>% filter(SplitRev == 8)Selecting subset
# ## Selecting
# otu <- otu2
# env <- env2
format data correctly
#env$coords.x1<-as.numeric(env$coords.x1)
#env$coords.x2<-as.numeric(env$coords.x2)
Abundance weighting
Make function You might have to drop variables that have been imported as character
otu_pa <- decostand(x = otu[, -c(1)],
method = "pa")
# y = ax^w # power transformation formula
dt <- otu[, -c(1:2)] # species data to transform
x_mn <- min(dt[dt > 0])
x_mx <- max(dt)
rng <- 6 # abundance range
w <- log(rng) / (log(x_mx) - log(x_mn))
a <- x_mn^(-w)
# otu_6 <- a * dt[, -c(1:3)]^w
otu_6 <- a * dt^w
range(otu_6)[1] 0 6Ordination methods
DCA PA
Sys.time()[1] "2022-09-29 23:46:54 CEST"dca_pa <- decorana(veg = otu_pa)some species were removed because they were missing in the dataprint(dca_pa, head=T)
Call:
decorana(veg = otu_pa)
Detrended correspondence analysis with 26 segments.
Rescaling of axes with 4 iterations.
DCA1 DCA2 DCA3 DCA4
Eigenvalues 0.5113 0.3652 0.2255 0.2934
Decorana values 0.5496 0.3584 0.3320 0.2648
Axis lengths 5.6856 3.9595 2.4912 2.7604GNMDS PA
Distances - don’t run if loading saved object
## Bray-Curtis
dist_pa <- vegdist(x = otu_pa, method = "bray")
## Geodist
ep <- 0.8 # epsilon
geodist_pa <- isomapdist(dist = dist_pa, epsilon = ep)Save the result - don’t run if loading saved object
saveRDS(geodist_pa,
file = (file.path(dataPath,"inputs/BarentsDeep_geodist_pa.rds")))Ordination
monoMDS - don’t run if loading saved object
took 6.4mins with jonatan’s paralell solution (usually ~10mins)
#
# Sys.time()
# d <- 2
# mds_pa <- list()
#
# for (i in 1:100) {
# mds_pa[[i]]<-monoMDS(geodist_pa,
# matrix(c(runif(dim(otu_pa)[1]*d)),
# nrow = dim(otu_pa)[1]),
# k = d,
# model = "global",
# maxit = 2000,
# smin = 1e-7,
# sfgrmin = 1e-7)
# }
# Sys.time()
## monoMDS
# d <- 2
# mds_r6 <- list()
#
# Sys.time()
# for (i in 1:200) {
# mds_r6[[i]]<-monoMDS(geodist_r6,
# matrix(c(runif(dim(otu_6)[1]*d)),
# nrow = dim(otu_6)[1]),
# k = d,
# model = "global",
# maxit = 2000,
# smin = 1e-7,
# sfgrmin = 1e-7)
# }
# Sys.time()
#Jonatan's upgrade for speed - parallel processing of mds reptitions
library(furrr) # Package to run non sequential functions in parallel
#library(purrr)
plan(multisession)
d <- 2
i <-200 # number of reps
List_geodist_pa <- lapply(seq_len(i), function(X) geodist_pa) # makes a list with the input data repeated as many times as reps wanted
start_t<-Sys.time()
mds_pa<-furrr::future_map(List_geodist_pa,
function(x) monoMDS(x,
matrix(c(runif(dim(otu_6)[1]*d)),
nrow = dim(otu_6)[1]),
k = d,
model = "global",
maxit = 2000,
smin = 1e-7,
sfgrmin = 1e-7),
.progress = TRUE)
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Progress: ---------------------------------------------------------------- 100%UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".Sys.time() - start_tTime difference of 19.40465 minsSave the result - don’t run if loading saved object
can take a few mins
saveRDS(mds_pa,
file = file.path(dataPath,"inputs/BarentsDeep_mds_pa.rds"))Best nmds solution - PA
Make function?
# Loading geodist object
# geodist_nfi <- readRDS(file = "../SplitRev2_geodist_pa_full.rds")# Loading mds results
# mds <- readRDS(file = "../SplitRev2_mds_pa_full.rds")
## Extracting the stress of each nmds iteration
mds_stress_pa<-unlist(lapply(mds_pa, function(v){v[[22]]}))
ordered_pa <-order(mds_stress_pa)
## Best, second best, and worst solution
mds_stress_pa[ordered_pa[1]][1] 0.2718277mds_stress_pa[ordered_pa[2]][1] 0.2731914mds_stress_pa[ordered_pa[10]][1] 0.2739577## Scaling of axes to half change units and varimax rotation by postMDS
mds_best_pa<-postMDS(mds_pa[[ordered_pa[1]]],
geodist_pa,
pc = TRUE,
halfchange = TRUE,
threshold = ep) # Is this threshold related to the epsilon above?
mds_best_pa
Call:
monoMDS(dist = x, y = matrix(c(runif(dim(otu_6)[1] * d)), nrow = dim(otu_6)[1]), k = d, model = "global", maxit = 2000, smin = 1e-07, sfgrmin = 1e-07)
Non-metric Multidimensional Scaling
1676 points, dissimilarity ‘bray shortest isomap’, call ‘isomapdist(dist = dist_pa, epsilon = ep)’
Dimensions: 2
Stress: 0.2718277
Stress type 1, weak ties
Scores scaled to unit root mean square, rotated to principal components
Stopped after 106 iterations: Scale factor of gradient nearly zero (< sfgrmin)mds_secbest_pa <- postMDS(mds_pa[[ordered_pa[2]]],
geodist_pa,
pc = TRUE,
halfchange = TRUE,
threshold = ep)
mds_secbest_pa
Call:
monoMDS(dist = x, y = matrix(c(runif(dim(otu_6)[1] * d)), nrow = dim(otu_6)[1]), k = d, model = "global", maxit = 2000, smin = 1e-07, sfgrmin = 1e-07)
Non-metric Multidimensional Scaling
1676 points, dissimilarity ‘bray shortest isomap’, call ‘isomapdist(dist = dist_pa, epsilon = ep)’
Dimensions: 2
Stress: 0.2731914
Stress type 1, weak ties
Scores scaled to unit root mean square, rotated to principal components
Stopped after 198 iterations: Scale factor of gradient nearly zero (< sfgrmin)## Procrustes comparisons
procr_pa <- procrustes(mds_best_pa,
mds_secbest_pa,
permutations=999)
protest(mds_best_pa,
mds_secbest_pa,
permutations=999)
Call:
protest(X = mds_best_pa, Y = mds_secbest_pa, permutations = 999)
Procrustes Sum of Squares (m12 squared): 0.105
Correlation in a symmetric Procrustes rotation: 0.9461
Significance: 0.001
Permutation: free
Number of permutations: 999plot(procr_pa)
png(file=file.path(dataPath,"outputs/BarentsDeep_procrustes_pa.png"), width=1000, height=700)plot(procr_pa)
dev.off()png
2 
Correlation of axis: DCA vs NMDS - PA
# Extracting ordination axis
ax <- 2
axis_pa <- cbind(mds_best_pa$points,
scores(dca_pa,
display = "sites",
origin = TRUE)[, 1:ax])
ggcorr(axis_pa,
method=c("everything","kendall"),
label = TRUE,
label_size = 3,
label_color = "black",
nbreaks = 8,
label_round = 3,
low = "red",
mid = "white",
high = "green")
NA
NASave the figure
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_correlationPCAvsNMDS_PA.png"),
device = "png",
dpi=300 )Saving 7 x 7 in image# Switching direction of NMDS1
# mds_best$points[, 1] <- -mds_best$points[, 1]DCA R6
dca_r6 <- decorana(veg = otu_6)some species were removed because they were missing in the dataprint(dca_r6, head=T)
Call:
decorana(veg = otu_6)
Detrended correspondence analysis with 26 segments.
Rescaling of axes with 4 iterations.
DCA1 DCA2 DCA3 DCA4
Eigenvalues 0.5303 0.2641 0.3859 0.3160
Decorana values 0.5702 0.3993 0.3733 0.3032
Axis lengths 5.3863 1.5392 4.3342 5.7681GNMDS R6
Distances - don’t run if loading saved object
## Bray-Curtis
dist_r6 <- vegdist(x = otu_6, method = "bray")
## Geodist
ep <- 0.80 # epsilon
geodist_r6 <- isomapdist(dist = dist_r6, epsilon = ep)Save the result - don’t run if loading saved object
saveRDS(geodist_r6,
file = (file.path(dataPath,"inputs/BarentsDeep_geodist_r6.rds")))Ordination
200 reps - don’t run if loading saved object
Jonatan’s parallel solution took 5.4mins, before it took 10mins
## monoMDS
# d <- 2
# mds_r6 <- list()
#
# Sys.time()
# for (i in 1:200) {
# mds_r6[[i]]<-monoMDS(geodist_r6,
# matrix(c(runif(dim(otu_6)[1]*d)),
# nrow = dim(otu_6)[1]),
# k = d,
# model = "global",
# maxit = 2000,
# smin = 1e-7,
# sfgrmin = 1e-7)
# }
# Sys.time()
#Jonatan's upgrade for speed - parallel processing of mds reptitions
#library(furrr) # Package to run non sequential functions in parallel
#library(purrr)
plan(multisession)
d <- 2
i <-200 # number of reps
List_geodist_r6 <- lapply(seq_len(i), function(X) geodist_r6) # makes a list with the input data repeated as many times as reps wanted
start_t<-Sys.time()
mds_r6<-furrr::future_map(List_geodist_r6,
function(x) monoMDS(x,
matrix(c(runif(dim(otu_6)[1]*d)),
nrow = dim(otu_6)[1]),
k = d,
model = "global",
maxit = 2000,
smin = 1e-7,
sfgrmin = 1e-7),
.progress = TRUE)
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Progress: ---------------------------------------------------------------- 100%UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".UNRELIABLE VALUE: Future (㤼㸱<none>㤼㸲) unexpectedly generated random numbers without specifying argument 'seed'. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'seed=NULL', or set option 'future.rng.onMisuse' to "ignore".Sys.time() - start_tTime difference of 15.123 minsSave the result - don’t run if loading saved object
saveRDS(mds_r6,
file = file.path(dataPath,"inputs/BarentsDeep_mds_r6.rds")) Best nmds solution r6 200 rep
# Loading geodist object
# geodist_nfi <- readRDS(file = "../SplitRev2_geodist_nfi.rds")
# Loading mds results
# mds <- readRDS(file = "../SplitRev2_mds.rds")
## Extracting the stress of each nmds iteration
mds_stress_r6<-unlist(lapply(mds_r6, function(v){v[[22]]}))
ordered_r6 <-order(mds_stress_r6)
## Best, second best, and worst solution
mds_stress_r6[ordered_r6[1]][1] 0.2669464mds_stress_r6[ordered_r6[2]][1] 0.2678038mds_stress_r6[ordered_r6[100]][1] 0.2742299## Scaling of axes to half change units and varimax rotation by postMDS
mds_best_r6<-postMDS(mds_r6[[ordered_r6[1]]],
geodist_r6,
pc = TRUE,
halfchange = TRUE,
threshold = ep) # Is this threshold related to the epsilon above?
mds_best_r6
Call:
monoMDS(dist = x, y = matrix(c(runif(dim(otu_6)[1] * d)), nrow = dim(otu_6)[1]), k = d, model = "global", maxit = 2000, smin = 1e-07, sfgrmin = 1e-07)
Non-metric Multidimensional Scaling
1676 points, dissimilarity ‘bray shortest isomap’, call ‘isomapdist(dist = dist_r6, epsilon = ep)’
Dimensions: 2
Stress: 0.2669464
Stress type 1, weak ties
Scores scaled to unit root mean square, rotated to principal components
Stopped after 147 iterations: Scale factor of gradient nearly zero (< sfgrmin)mds_secbest_r6<-postMDS(mds_r6[[ordered_r6[2]]],
geodist_r6,
pc = TRUE,
halfchange = TRUE,
threshold = ep)
mds_secbest_r6
Call:
monoMDS(dist = x, y = matrix(c(runif(dim(otu_6)[1] * d)), nrow = dim(otu_6)[1]), k = d, model = "global", maxit = 2000, smin = 1e-07, sfgrmin = 1e-07)
Non-metric Multidimensional Scaling
1676 points, dissimilarity ‘bray shortest isomap’, call ‘isomapdist(dist = dist_r6, epsilon = ep)’
Dimensions: 2
Stress: 0.2678038
Stress type 1, weak ties
Scores scaled to unit root mean square, rotated to principal components
Stopped after 111 iterations: Scale factor of gradient nearly zero (< sfgrmin)## Procrustes comparisons
procr_r6 <- procrustes(mds_best_r6,
mds_secbest_r6,
permutations=999)
protest(mds_best_r6,
mds_secbest_r6,
permutations=999)
Call:
protest(X = mds_best_r6, Y = mds_secbest_r6, permutations = 999)
Procrustes Sum of Squares (m12 squared): 0.05042
Correlation in a symmetric Procrustes rotation: 0.9745
Significance: 0.001
Permutation: free
Number of permutations: 999plot(procr_r6)
png(file.path(dataPath,"outputs/BarentsDeep_procrustes_r6.png"), width=1000, height=700,) #added 1000plot(procr_r6)
dev.off()png
2 
#### 1000 reps - don’t run if loading saved object Currently commented out as it gained nothing but took extra time. Can be removed in due course.
Correlation of axis: DCA vs NMDS
retain the 200 rep version
# Extracting ordination axis
ax <- 2
axis_r6 <- cbind(mds_best_r6$points,
scores(dca_r6,
display = "sites",
origin = TRUE)[, 1:ax])
ggcorr(axis_r6,
method=c("everything","kendall"),
label = TRUE,
label_size = 3,
label_color = "black",
nbreaks = 8,
label_round = 3,
low = "red",
mid = "white",
high = "green")
Save the figure
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_correlationPCAvsNMDS_r6.png"),
device = "png",
dpi=300 )Saving 7 x 7 in image# Switching direction of NMDS1
# mds_best$points[, 1] <- -mds_best$points[, 1]Plotting DCA & GNMDS
## Adding scores to data frame
otu_6$gnmds1 <- mds_best_r6$points[, 1]
otu_6$gnmds2 <- mds_best_r6$points[, 2]
otu_6$dca1 <- scores(dca_r6, display = "sites", origin = TRUE)[, 1]
otu_6$dca2 <- scores(dca_r6, display = "sites", origin = TRUE)[, 2]
p_gnmds_r6 <- ggplot(data = otu_6,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS",
subtitle = "First run") +
geom_point(colour = "red") +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")
p_dca_r6 <- ggplot(data = otu_6,
aes(x = -dca1,
y = dca2)) +
theme_classic() +
coord_fixed() +
ggtitle("DCA",
subtitle = "First run") +
geom_point(colour = "red") +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")
p_gnmds_r6 + p_dca_r6
NA
NASave the figure
ggsave(file.path(dataPath,"outputs/BarentsDeep_gnmds_dca_r6.png"),
device = "png",
dpi=300)Saving 7 x 7 in imageSpecies-environment relationships
Selecting ordination
ord <- mds_best_r6
## Axis scores if selected ord is GNMDS
axis <- ord$points %>% as.data.frame
## Axis scores if selected ord is DCA
# axis <- scores(ord,
# display = "sites",
# origin = TRUE)[, 1:ax])
Create additional variables
decided to make MLD-bathy vars
env<-env %>%
mutate ("MLDmean_bathy"=MLDmean_Robinson-(bathy*-1),
"MLDmin_bathy"=MLDmin_Robinson-(bathy*-1),
"MLDmax_bathy"=MLDmax_Robinson-(bathy*-1))
env$MLDmean_bathy<-cut(env$MLDmean_bathy,
breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
labels=c('belowMLD','onPycno','inMixLayer'))
env$MLDmin_bathy<-cut(env$MLDmin_bathy,
breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
labels=c('belowMLD','onPycno','inMixLayer'))
env$MLDmax_bathy<-cut(env$MLDmax_bathy,
breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
labels=c('belowMLD','onPycno','inMixLayer'))
env$swDensRob_avs<-swRho(salinity=env$Smean_Robinson,
temperature=env$Tmean_Robinson,
pressure=(env$bathy*-1),
eos="unesco")
Correlation ordination axes and environmental variables
Removing non-env vars
env_cont<-env%>% select(-c(landscape,sedclass,gmorph, MLDmean_bathy, MLDmax_bathy, MLDmin_bathy, #categorical
optional, #not a var
MLDmax_Robinson, MLDmean_Robinson, MLDmin_Robinson, #replaced by new vars
MLDsd_Robinson, #not meaningful
SampID))
env_cont<-env_cont%>% mutate_if(is.integer,as.numeric)
env_corr <- env_cont #%>% select(-c(SampID))
# env_corr$coords.x1<-as.numeric(env_corr$coords.x1)
# env_corr$coords.x2<-as.numeric(env_corr$coords.x2)
env_corr[(!is.numeric(env_corr)),]Correlations
# Vector to hold correlations
cor_ax1 <- NULL
cor_ax2 <- NULL
pv_ax1 <- NULL
pv_ax2 <- NULL
# NMDS1
for( i in seq(length(env_corr))) {
ct.i <- cor.test(axis$MDS1,
env_corr[, i],
method = "kendall")
cor_ax1[i] <- ct.i$estimate
pv_ax1[i] <- ct.i$p.value
}
# NMDS2
for( i in seq(length(env_corr))) {
ct.i <- cor.test(axis$MDS2,
env_corr[, i],
method = "kendall")
cor_ax2[i] <- ct.i$estimate
pv_ax2[i] <- ct.i$p.value
}
cor_tab <- data.frame(env = names(env_corr),
ord_ax1 = cor_ax1,
pval_ax1 = pv_ax1,
ord_ax2 = cor_ax2,
pval_ax2 = pv_ax2)
cor_tab
write.csv(x = cor_tab,
file = file.path(dataPath,"inputs/BarentsDeep_cor-table_r6_200rep_MLD-bathy.csv"),
row.names = FALSE)Dot chart to check for gaps in correlation
cor_a1_sort<-cor_tab%>%
mutate(abs_ord_ax1=abs(ord_ax1),
abs_ord_ax2=abs(ord_ax2)) %>%
arrange(desc(abs_ord_ax1))
cor_a2_sort<-cor_tab%>%
mutate(abs_ord_ax1=abs(ord_ax1),
abs_ord_ax2=abs(ord_ax2)) %>%
arrange(desc(abs_ord_ax2))
dotchart(cor_a1_sort$abs_ord_ax1, main="Absolute (+/-) correlations between envVars and gnmds axis 1")
cor_cut<-0.49 #decide
cor_sel<-subset(cor_a1_sort,abs_ord_ax1>cor_cut)
cor_sel
as.data.frame(cor_sel$env)Sel env var (top corr)
env_os <- env[, cor_sel$env]
env_osstr(env_os)'data.frame': 1676 obs. of 26 variables:
$ swDensRob_avs : num 1029 1029 1029 1029 1029 ...
$ bathy : num -295 -292 -292 -270 -270 ...
$ BO22_silicatemean_bdmean : num 5.71 5.71 5.71 5.7 5.7 ...
$ BO22_silicaterange_bdmean : num 5.71 5.71 5.71 5.7 5.7 ...
$ BO22_silicateltmin_bdmean : num 4.83 4.83 4.83 4.82 4.81 ...
$ BO22_chloltmax_bdmean : num 0.196 0.198 0.198 0.21 0.21 ...
$ BO22_carbonphytoltmax_bdmean: num 0.581 0.585 0.584 0.621 0.62 ...
$ BO22_phosphateltmax_bdmean : num 0.694 0.694 0.694 0.696 0.696 ...
$ BO22_chlomean_bdmean : num 0.0805 0.0809 0.0808 0.0846 0.0845 ...
$ BO22_chlorange_bdmean : num 0.0805 0.0809 0.0808 0.0846 0.0845 ...
$ BO22_carbonphytomean_bdmean : num 0.233 0.235 0.234 0.245 0.245 ...
$ BO22_carbonphytorange_bdmean: num 0.233 0.235 0.234 0.245 0.245 ...
$ BO22_phosphatemean_bdmean : num 0.65 0.65 0.65 0.649 0.649 ...
$ BO22_phosphaterange_bdmean : num 0.65 0.65 0.65 0.649 0.649 ...
$ BO22_silicateltmax_bdmean : num 6.55 6.55 6.55 6.58 6.58 ...
$ BO22_phosphateltmin_bdmean : num 0.594 0.593 0.593 0.593 0.592 ...
$ BO22_ppltmin_ss : num 0 0 0 0 0 0 0 0 0 0 ...
$ BO22_nitrateltmin_bdmean : num 7.65 7.64 7.64 7.63 7.63 ...
$ BO22_nitratemean_bdmean : num 8.16 8.15 8.15 8.12 8.12 ...
$ BO22_nitraterange_bdmean : num 8.16 8.15 8.15 8.12 8.12 ...
$ BO22_chloltmin_bdmean : num 0.0315 0.0316 0.0316 0.0325 0.0325 ...
$ BO22_nitrateltmax_bdmean : num 8.7 8.69 8.69 8.65 8.65 ...
$ BO22_ppltmax_bdmean : num 2.16e-05 2.24e-05 2.24e-05 3.05e-05 3.05e-05 ...
$ BO22_ppmean_bdmean : num 2.12e-06 2.23e-06 2.21e-06 3.00e-06 3.00e-06 ...
$ BO22_pprange_bdmean : num 2.12e-06 2.23e-06 2.21e-06 3.00e-06 3.00e-06 ...
$ BO22_carbonphytoltmin_bdmean: num 0.0869 0.0872 0.0871 0.0895 0.0895 ...Ordisurfs top corr
ordsrfs <- list(length = ncol(env_os))
for (i in seq(ncol(env_os))) {
os.i <- gg_ordisurf(ord = ord,
env.var = env_os[, i],
pt.size = 1,
# binwidth = 0.05,
var.label = names(env_os)[i],
gen.text.size = 10,
title.text.size = 15,
leg.text.size = 10)
ordsrfs[[i]] <- os.i$plot
}
ordsrfs_plt <- ggarrange(plotlist = ordsrfs,
nrow = 5,
ncol = 6)
ordsrfs_plt
Save some outputs
ggexport(ordsrfs_plt,
filename = file.path(dataPath,"outputs/BarentsDeep_ordisurfs_top_corr.png"),
width = 2500,
height = 2000)
Sel env var (manual)
env_os_m <- env[,c("Tmean_Robinson", #top corr
"salt_max", #top corr
"Smax_Robinson", #comparison to top corr
"swDensRob_avs", #top corr
"BO22_icecoverltmax_ss",#top corr ax2
"BO22_icecovermean_ss",#top corr
"BO22_dissoxmean_bdmean",#top corr
#"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
"BO22_ppltmin_ss", #top corr
"X.y", #comparison to Y
"Y", #top corr
"spd_std", #top corr ax2 (blended model)
"CSpdsd_Robinson", #comparison to top corr ax2 (blended model)
"mud", #highest sed var ax1 + corr
"gravel",#highest sed var ax1 - corr
"BO22_silicateltmax_bdmean", #just under top corr ax1
"bathy" #intuitive for comparisons
)]
env_os_mstr(env_os_m)'data.frame': 1676 obs. of 16 variables:
$ Tmean_Robinson : num 4.07 4.07 4.06 3.9 3.9 ...
$ salt_max : num 35 35 35 35 35 ...
$ Smax_Robinson : num 35.1 35.1 35.1 35.1 35.1 ...
$ swDensRob_avs : num 1029 1029 1029 1029 1029 ...
$ BO22_icecoverltmax_ss : num 3.86e-06 3.89e-06 3.96e-06 7.71e-06 7.49e-06 ...
$ BO22_icecovermean_ss : num 5.82e-07 5.55e-07 5.82e-07 1.06e-06 1.01e-06 ...
$ BO22_dissoxmean_bdmean : num 310 310 310 311 311 ...
$ BO22_ppltmin_ss : num 0 0 0 0 0 0 0 0 0 0 ...
$ X.y : num 1075461 1075261 1075261 1073461 1073261 ...
$ Y : num 8125134 8125334 8125534 8137734 8137534 ...
$ spd_std : num 0.054 0.0541 0.0543 0.0516 0.0516 ...
$ CSpdsd_Robinson : num 0.0222 0.0223 0.0224 0.0163 0.0165 ...
$ mud : num 69.5 60 60 69.5 69.5 69.5 60 60 60 69.5 ...
$ gravel : num 0.5 15 15 0.5 0.5 0.5 15 15 15 0.5 ...
$ BO22_silicateltmax_bdmean: num 6.55 6.55 6.55 6.58 6.58 ...
$ bathy : num -295 -292 -292 -270 -270 ...Ordisurfs manually selected
ordsrfs_m <- list(length = ncol(env_os_m))
for (i in seq(ncol(env_os_m))) {
os.i_m <- gg_ordisurf(ord = ord,
env.var = env_os_m[, i],
pt.size = 1,
# binwidth = 0.05,
var.label = names(env_os_m)[i],
gen.text.size = 10,
title.text.size = 15,
leg.text.size = 10)
ordsrfs_m[[i]] <- os.i_m$plot
}
ordsrfs_plt_m <- ggarrange(plotlist = ordsrfs_m,
nrow = 4,
ncol = 4)
ordsrfs_plt_m
Save some outputs
ggexport(ordsrfs_plt_m,
filename = file.path(dataPath,"outputs/BarentsDeep_ordisurfs_man_sel_domean.png"),
width = 2000,
height = 2000)
Envfit
## Select if any var should be excluded from envfit (makes less busy to read)
env_os_m_envfit<-env_os_m [,c("Tmean_Robinson", #top corr
"salt_max", #top corr
"Smax_Robinson", #comparison to top corr
"swDensRob_avs", #top corr
"BO22_icecoverltmax_ss",#top corr ax2
#"BO22_icecovermean_ss",#top corr
"BO22_dissoxmean_bdmean",#top corr
#"BO22_dissoxltmin_bdmean",#top corr
#"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
"BO22_ppltmin_ss", #top corr
"X.y", #comparison to Y
"Y", #top corr
"spd_std", #top corr ax2 (blended model)
# "CSpdsd_Robinson", #comparison to top corr ax2 (blended model)
"mud", #highest sed var ax1 + corr
"gravel",#highest sed var ax1 - corr
"BO22_silicateltmax_bdmean", #just under top corr ax1
"bathy" #intuitive for comparisons
)]
colnames(env_os_m_envfit)<-c("T", #top corr
"sMx", #top corr
"SmaxR", #comparison to top corr
"swDensR", #top corr
"icecovmax",#top corr ax2
#"icecovav",#top corr
"dissoxav",#top corr
#"dissoxmin",#top corr
#"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
"ppltmin", #top corr
"X", #comparison to Y
"Y", #top corr
"spd_std", #top corr ax2 (blended model)
# "CSsd", #comparison to top corr ax2 (blended model)
"mud", #highest sed var ax1 + corr
"gravel",#highest sed var ax1 - corr
"SiLtmax", #just under top corr ax1
"bathy" #intuitive for comparisons
)
## Envfot plot
gg_envfit(ord = ord,
env = env_os_m_envfit,
pt.size = 1)
## Envfit analysis
ef <- envfit(ord = ord,
env = env_os_m_envfit,
# na.rm = TRUE
)
efDF <- as.data.frame(scores(ef,
display = "vectors"))Save the plot
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_EnvFit_man_sel_cln_domean.png"),
device = "png",
dpi=300 )Saving 7 x 7 in imageCategorical envVar visualised on the mdsplots
use dataset with categorical var included
env_vis<-env
env_vis$gnmds1 <- otu_6$gnmds1
env_vis$gnmds2 <- otu_6$gnmds2
env_vis$dca1 <- otu_6$dca1
env_vis$dca2 <- otu_6$dca2gnmds w mld mean - bathy
dca_int <- ggplot(data = env_vis,
aes(x = dca1,
y = dca2)) +
theme_classic() +
coord_fixed() +
ggtitle("Interactive DCA sample ID plot",
subtitle = "First run") +
geom_point(aes(colour = factor(SampID))) +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")
ggplotly(dca_int)gnmds w mld mean - bathy
p_mld <- ggplot(data = env_vis,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS coloured by proximity to mixed layer depth",
subtitle = "First run") +
geom_point(aes(colour = MLDmean_bathy)) +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")
p_mld
gnmds w sedclass
deal with sedclass codes
env_vis<- env_vis %>%
mutate(
sedclassName = case_when(
sedclass == "1" ~ "SedCoverR",
sedclass == "5" ~ "Rock",
sedclass == "20" ~ "Mud",
sedclass == "21" ~ "MwBlock",
sedclass == "40" ~ "sMud",
sedclass == "80" ~ "mSand",
sedclass == "100" ~ "Sand",
sedclass == "110" ~ "gMud",
sedclass == "115" ~ "gsMud",
sedclass == "120" ~ "gmSand",
sedclass == "130" ~ "gSand",
sedclass == "150" ~ "MSG",
sedclass == "160" ~ "sGravel",
sedclass == "170" ~ "Gravel",
sedclass == "175" ~ "GravBlock",
sedclass == "185" ~ "SGBmix",
sedclass == "205" ~ "S/MwB",
sedclass == "206" ~ "S/MwG/B",
sedclass == "215" ~ "SGBalt",
sedclass == "300" ~ "HardSed",
sedclass == "500" ~ "Biogenic"
)
)colour palette to cope with up to 25 categorical colours
c25 <- c(
"dodgerblue2", "#E31A1C", # red
"green4",
"#6A3D9A", # purple
"#FF7F00", # orange
"black", "gold1",
"skyblue2", "#FB9A99", # lt pink
"palegreen2",
"#CAB2D6", # lt purple
"#FDBF6F", # lt orange
"gray70", "khaki2",
"maroon", "orchid1", "deeppink1", "blue1", "steelblue4",
"darkturquoise", "green1", "yellow4", "yellow3",
"darkorange4", "brown"
)
p_sed <- ggplot(data = env_vis,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS coloured by sediment class",
subtitle = "First run") +
geom_point(aes(colour = factor(sedclassName))) +
scale_colour_manual(values=c25)+
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")+
guides(colour=guide_legend(ncol=2))
p_sed
gnmds w lanscape
deal with sedclass codes
env_vis<- env_vis %>%
mutate(
landscapeName = case_when(
landscape == "1" ~ "Strandflat",
landscape == "21" ~ "ContSlope",
landscape == "22" ~ "Canyon",
landscape == "31" ~ "Valley",
landscape == "32" ~ "Fjord",
landscape == "41" ~ "DeepPlain",
landscape == "42" ~ "SlopePlain",
landscape == "43" ~ "ShelfPlain",
landscape == "431" ~ "shallowValley"
)
)
p_land <- ggplot(data = env_vis,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS coloured by landscape class",
subtitle = "First run") +
geom_point(aes(colour = factor(landscapeName))) +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")+
guides(colour=guide_legend(ncol=2))
p_land
Gnmds w gmorph
p_gmo <- ggplot(data = env_vis,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS coloured by landscape class",
subtitle = "First run") +
geom_point(aes(colour = factor(gmorph))) +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")+
guides(colour=guide_legend(ncol=2))
p_gmo
cat_var_plots<-p_mld+p_sed+p_land+p_gmoSave the plot
ggexport(cat_var_plots,
filename = file.path(dataPath,"outputs/BarentsDeep_gnmds_catvar.png"),
width = 1000,
height = 800)Sample identification in the mdsplot
p_gmo <- ggplot(data = env_vis,
aes(x = gnmds1,
y = gnmds2)) +
theme_classic() +
coord_fixed() +
ggtitle("GNMDS coloured by sample",
subtitle = "First run") +
geom_point(aes(colour = factor(SampID))) +
geom_vline(xintercept = 0,
linetype = 2,
colour = "lightgray") +
geom_hline(yintercept = 0,
linetype = 2,
colour = "lightgray")+
guides(colour="none", size="none")
ggplotly(p_gmo)---
title: "MAREANO - NiN - loDens <1031.8 without SpitsbergenBanken outliers"
authors: Thijs van Son, Rune Halvorsen, Rebecca Ross, Genoveva Gonzales-Mirelis, Margaret Dolan
date: "Last Rendered on `r format(Sys.time(), '%d %B, %Y')`"
output: 
  html_notebook: 
    toc: yes
    toc_depth: 2
    toc_float: yes
    fig_width: 7
    fig_height: 7
always_allow_html: true 
---

## Packages
```{r}
library(dplyr)
library(ggcorrplot)
library(ggpubr)
library(patchwork)
# library("here")
# library("bookdown")
# library("downloadthis")
library(vegan)
library(plyr)
library(e1071)
library(tidyverse)
library(viridis)
library(GGally)
library(ggrepel)
library(ggordiplots)
library(readr)
library(RColorBrewer)
library(oce) 
library(plotly)
library(purrr)
library(furrr)


source("gg_ordisurf_viridis.R")
```

LOAD OBJECTS IF HAVE RUN ALREADY
```{r}
geodist_pa<-readRDS(file.path(dataPath,"inputs/BarentsDeep_geodist_pa.rds"))
geodist_r6<-readRDS(file.path(dataPath,"inputs/BarentsDeep_geodist_r6.rds"))

mds_pa<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_pa.rds"))
mds_r6<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_r6.rds"))
#mds_r6_1000<-readRDS(file.path(dataPath,"inputs/BarentsDeep_mds_r6_1000rep.rds"))

ep<-0.8 #epsilon (to avoid having to find and run just this line from code blocks where the mds objects were made)
```

## Combine Barents and Deep datasets
```{r}
env_sort1 <- read.csv(file.path(dataPath,"inputs/SPLITS/BARENTS/LDnoSBhi02_env_sort_2022-09-29.csv")) %>% as.data.frame
otu_sort1 <- read.csv(file.path(dataPath,"inputs/SPLITS/BARENTS/LDnoSBhi02_otu_sort_2022-09-29.csv")) %>% as.data.frame

env_sort2 <- read.csv(file.path(dataPath,"inputs/SPLITS/DEEP/HDensDeep_env_sort_2022-09-29.csv")) %>% as.data.frame
otu_sort2 <- read.csv(file.path(dataPath,"inputs/SPLITS/DEEP/HDensDeep_otu_sort_2022-09-29.csv")) %>% as.data.frame

env_orig <- rbind(env_sort1,env_sort2)
otu_orig <- rbind(otu_sort1, otu_sort2)
```



```{r}
#joinedDat<-otu_orig%>% dplyr::left_join(.,env_orig, by="SampID")
joinedDat<- left_join(otu_orig,env_orig, by=c("SampID"="SampID"))

otu_sort<-joinedDat %>% select(c(2:391))
env_sort<-joinedDat %>% select(c(2,393:528))

# Sorting and ordering by SampID
# env_orig <- env_orig[order(env_orig$SampID), ]
# otu_orig <- otu_orig[order(otu_orig$SampID), ]

table(env_sort$SampID == otu_sort$SampID)


#must be same length and equal to unique sample number length - the ordered lists are assumed to be directly relatable on a row by row basis

dim(env_sort)
dim(otu_sort)
```






## Data
```{r}
table(env_sort$SampID2 == otu_sort$SampID)


#must be same length and equal to unique sample number length - the ordered lists are assumed to be directly relatable on a row by row basis

dim(env_sort)
dim(otu_sort)
```

#### remove any variables with not enough coverage
```{r}

env_sort <- env_sort %>% select(-c("BO22_lightbotmean_bdmean",
                               "BO22_lightbotltmax_bdmean",
                               "BO22_lightbotltmin_bdmean",
                               "BO22_lightbotrange_bdmean"))

env_sort$X.y<-env_sort$X.y.y
```


#### Plot map to check location of samples
```{r}
env_sort_locations<- ggplot(data = env_sort,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 2) +
  scale_colour_gradient2(low = "red",
                         mid = "yellow",
                         high = "green") +
  ggtitle("Location of samples in sorted env file")

env_sort_locations
```


### Data cleaning
```{r}
## Removing NAs
otuCompl <- otu_sort[complete.cases(env_sort[, -c(1:which(colnames(env_sort)=="bathy")-1)]), ]
envCompl <- env_sort[complete.cases(env_sort[, -c(1:which(colnames(env_sort)=="bathy")-1)]), ]

## Removing observations with less than 4 OTUs
sel <- rowSums(otuCompl[, -c(1)]) >= 4
otuSel <- otuCompl[sel, ]
envSel <- envCompl[sel, ]


## Removing phosphate
#envSel <- envSel %>% select(-phosphate_mean.tif)


dim(otuSel); dim(envSel)
```
#### Show what samples are left after complete cases and >4 OTUs filters
```{r}
env_sel_locations<- ggplot(data = envSel,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 2) +
  scale_colour_gradient2(low = "red",
                         mid = "yellow",
                         high = "green") +
  ggtitle("Location of samples in sorted env file")

env_sel_locations
```

#### Show what got removed in complete cases filter
```{r}
envCompl_ccrem<-env_sort%>%filter(!SampID%in%envCompl$SampID)

envCompl_ccrem_locations<- ggplot(data = envCompl_ccrem,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 2) +
  scale_colour_gradient2(low = "red",
                         mid = "yellow",
                         high = "green") +
  ggtitle("Location of removed complete case samples resulting in envSel file")

envCompl_ccrem_locations
```
#### Show what got removed in <4 OTUs filter


```{r}
inv.sel <- rowSums(otuCompl[, -c(1:2)]) < 4
env.invSel <- envCompl[inv.sel, ]

env.invSel_locations<- ggplot(data = env.invSel,
                      aes(x = X.y,
                          y = Y)) +
  theme_classic() +
  geom_point(aes(colour = bathy),
             size = 2) +
  scale_colour_gradient2(low = "red",
                         mid = "yellow",
                         high = "green") +
  ggtitle("Location of removed samples with <4 OTUs resulting in envSel file")

env.invSel_locations

```

### [Optional] Data thinning
```{r, eval=FALSE}
# otu_red <- otu1[1:500, ]
# otu <- otu_red
# 
# env_red <- env[1:500, ]
# env <- env_red
```

### Whole dataset for first run
```{r}
otu<-otuSel[,-c(1:which(colnames(otuSel)=="Acesta_excavata")-1)]
env<-envSel[, -c(2:(which(colnames(envSel)=="bathy")-1))]

table(is.na(otu))

table(is.na(env))

#str(otuSel)
#str(envSel)
```


### Splitting in subsets
```{r}
# ## Class 1
# otu1 <- subset(otuSel, envSel$SplitRev == 1)
# env1 <- envSel %>% filter(SplitRev == 1)
# 
# ## Class 2
# otu2 <- subset(otuSel, envSel$SplitRev == 2)
# env2 <- envSel %>% filter(SplitRev == 2)
# 
# ## Class 4
# otu3 <- subset(otuSel, envSel$SplitRev == 3)
# env3 <- envSel %>% filter(SplitRev == 3)
# 
# ## Class 4
# otu4 <- subset(otuSel, envSel$SplitRev == 4)
# env4 <- envSel %>% filter(SplitRev == 4)
# 
# ## Class 6
# otu6 <- subset(otuSel, envSel$SplitRev == 6)
# env6 <- envSel %>% filter(SplitRev == 6)
# 
# ## Class 7
# otu7 <- subset(otuSel, envSel$SplitRev == 7)
# env7 <- envSel %>% filter(SplitRev == 7)
# 
# ## Class 8
# otu8 <- subset(otuSel, envSel$SplitRev == 8)
# env8 <- envSel %>% filter(SplitRev == 8)
```

## Selecting subset
```{r}
# ## Selecting 
# otu <- otu2
# env <- env2

```

## format data correctly
```{r}
#env$coords.x1<-as.numeric(env$coords.x1)
#env$coords.x2<-as.numeric(env$coords.x2)


```


## Abundance weighting
Make function
You might have to drop variables that have been imported as character
```{r}

otu_pa <- decostand(x = otu[, -c(1)],
                    method = "pa")

# y = ax^w         # power transformation formula
dt <- otu[, -c(1:2)]          # species data to transform
x_mn <- min(dt[dt > 0])
x_mx <- max(dt)
rng <- 6           # abundance range
w <- log(rng) / (log(x_mx) - log(x_mn))
a <- x_mn^(-w)
# otu_6 <- a * dt[, -c(1:3)]^w
otu_6 <- a * dt^w
range(otu_6)
```

## Ordination methods
### DCA PA
```{r}
Sys.time()
dca_pa <- decorana(veg = otu_pa)

print(dca_pa, head=T)
```


### GNMDS PA

#### Distances - don't run if loading saved object

```{r}
## Bray-Curtis
dist_pa <- vegdist(x = otu_pa, method = "bray")

## Geodist
ep <- 0.8     # epsilon
geodist_pa <- isomapdist(dist = dist_pa, epsilon = ep)
```


##### Save the result - don't run if loading saved object
```{r}
saveRDS(geodist_pa,
        file = (file.path(dataPath,"inputs/BarentsDeep_geodist_pa.rds")))
```

#### Ordination

#### monoMDS - don't run if loading saved object
took 6.4mins with jonatan's paralell solution (usually ~10mins)
```{r}
# 
# Sys.time()
# d <- 2
# mds_pa <- list()
# 
# for (i in 1:100) {
#   mds_pa[[i]]<-monoMDS(geodist_pa,
#                     matrix(c(runif(dim(otu_pa)[1]*d)),
#                            nrow = dim(otu_pa)[1]),
#                     k = d,
#                     model = "global",
#                     maxit = 2000,
#                     smin = 1e-7,
#                     sfgrmin = 1e-7)
# }
# Sys.time()


## monoMDS
# d <- 2
# mds_r6 <- list()
# 
# Sys.time()
# for (i in 1:200) {
#   mds_r6[[i]]<-monoMDS(geodist_r6,
#                     matrix(c(runif(dim(otu_6)[1]*d)),
#                            nrow = dim(otu_6)[1]),
#                     k = d,
#                     model = "global",
#                     maxit = 2000,
#                     smin = 1e-7,
#                     sfgrmin = 1e-7)
# }
# Sys.time()

#Jonatan's upgrade for speed - parallel processing of mds reptitions
library(furrr) # Package to run non sequential functions in parallel
#library(purrr)
plan(multisession)

d <- 2

i <-200 # number of reps

List_geodist_pa <- lapply(seq_len(i), function(X) geodist_pa) # makes a list with the input data repeated as many times as reps wanted
start_t<-Sys.time()
mds_pa<-furrr::future_map(List_geodist_pa, 
                  function(x) monoMDS(x,
                                      matrix(c(runif(dim(otu_6)[1]*d)),
                                             nrow = dim(otu_6)[1]),
                                      k = d,
                                      model = "global",
                                      maxit = 2000,
                                      smin = 1e-7,
                                      sfgrmin = 1e-7),
                  .progress = TRUE)
Sys.time() - start_t



```

##### Save the result - don't run if loading saved object
can take a few mins
```{r}
saveRDS(mds_pa,
        file = file.path(dataPath,"inputs/BarentsDeep_mds_pa.rds"))
```


##### Best nmds solution - PA
Make function?
```{r, eval=TRUE}
# Loading geodist object
# geodist_nfi <- readRDS(file = "../SplitRev2_geodist_pa_full.rds")
```


```{r}
# Loading mds results
# mds <- readRDS(file = "../SplitRev2_mds_pa_full.rds")

## Extracting the stress of each nmds iteration
mds_stress_pa<-unlist(lapply(mds_pa, function(v){v[[22]]})) 

ordered_pa <-order(mds_stress_pa)

## Best, second best, and worst solution
mds_stress_pa[ordered_pa[1]]
mds_stress_pa[ordered_pa[2]]
mds_stress_pa[ordered_pa[10]]

## Scaling of axes to half change units and varimax rotation by postMDS
mds_best_pa<-postMDS(mds_pa[[ordered_pa[1]]],
                  geodist_pa, 
                  pc = TRUE, 
                  halfchange = TRUE, 
                  threshold = ep)     # Is this threshold related to the epsilon above?
mds_best_pa

mds_secbest_pa <- postMDS(mds_pa[[ordered_pa[2]]],
                          geodist_pa, 
                          pc = TRUE, 
                          halfchange = TRUE, 
                          threshold = ep)
mds_secbest_pa

## Procrustes comparisons
procr_pa <- procrustes(mds_best_pa,
                    mds_secbest_pa,
                    permutations=999)
protest(mds_best_pa,
        mds_secbest_pa,
        permutations=999)

plot(procr_pa)

png(file=file.path(dataPath,"outputs/BarentsDeep_procrustes_pa.png"), width=1000, height=700)
plot(procr_pa)
dev.off()
```

##### Correlation of axis: DCA vs NMDS - PA
```{r}
# Extracting ordination axis
ax <- 2
axis_pa <- cbind(mds_best_pa$points,
                 scores(dca_pa,
                        display = "sites",
                        origin = TRUE)[, 1:ax])

ggcorr(axis_pa, 
       method=c("everything","kendall"), 
       label = TRUE,
       label_size = 3, 
       label_color = "black",  
       nbreaks = 8,
       label_round = 3,
       low = "red",
       mid = "white",
       high = "green")


```

##### Save the figure
```{r}
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_correlationPCAvsNMDS_PA.png"),
       device = "png",
       dpi=300 )

# Switching direction of NMDS1
# mds_best$points[, 1] <- -mds_best$points[, 1]
```

### DCA R6
```{r}
dca_r6 <- decorana(veg = otu_6)

print(dca_r6, head=T)
```


### GNMDS R6
#### Distances - don't run if loading saved object
```{r}
## Bray-Curtis
dist_r6 <- vegdist(x = otu_6, method = "bray")

## Geodist
ep <- 0.80     # epsilon
geodist_r6 <- isomapdist(dist = dist_r6, epsilon = ep)
```

##### Save the result - don't run if loading saved object
```{r}
saveRDS(geodist_r6,
        file = (file.path(dataPath,"inputs/BarentsDeep_geodist_r6.rds")))
```


#### Ordination 

##### 200 reps - don't run if loading saved object
Jonatan's parallel solution took 5.4mins, before it took 10mins


```{r}
## monoMDS
# d <- 2
# mds_r6 <- list()
# 
# Sys.time()
# for (i in 1:200) {
#   mds_r6[[i]]<-monoMDS(geodist_r6,
#                     matrix(c(runif(dim(otu_6)[1]*d)),
#                            nrow = dim(otu_6)[1]),
#                     k = d,
#                     model = "global",
#                     maxit = 2000,
#                     smin = 1e-7,
#                     sfgrmin = 1e-7)
# }
# Sys.time()

#Jonatan's upgrade for speed - parallel processing of mds reptitions
#library(furrr) # Package to run non sequential functions in parallel
#library(purrr)
plan(multisession)

d <- 2

i <-200 # number of reps

List_geodist_r6 <- lapply(seq_len(i), function(X) geodist_r6) # makes a list with the input data repeated as many times as reps wanted
start_t<-Sys.time()
mds_r6<-furrr::future_map(List_geodist_r6, 
                  function(x) monoMDS(x,
                                      matrix(c(runif(dim(otu_6)[1]*d)),
                                             nrow = dim(otu_6)[1]),
                                      k = d,
                                      model = "global",
                                      maxit = 2000,
                                      smin = 1e-7,
                                      sfgrmin = 1e-7),
                  .progress = TRUE)
Sys.time() - start_t

```

##### Save the result - don't run if loading saved object
```{r}
saveRDS(mds_r6,
        file = file.path(dataPath,"inputs/BarentsDeep_mds_r6.rds")) 
```

##### Best nmds solution r6 200 rep 
```{r, eval=TRUE}
# Loading geodist object
# geodist_nfi <- readRDS(file = "../SplitRev2_geodist_nfi.rds")

# Loading mds results
# mds <- readRDS(file = "../SplitRev2_mds.rds")

## Extracting the stress of each nmds iteration
mds_stress_r6<-unlist(lapply(mds_r6, function(v){v[[22]]})) 

ordered_r6 <-order(mds_stress_r6)

## Best, second best, and worst solution
mds_stress_r6[ordered_r6[1]]
mds_stress_r6[ordered_r6[2]]
mds_stress_r6[ordered_r6[100]]

## Scaling of axes to half change units and varimax rotation by postMDS
mds_best_r6<-postMDS(mds_r6[[ordered_r6[1]]],
                     geodist_r6, 
                     pc = TRUE, 
                     halfchange = TRUE, 
                     threshold = ep)     # Is this threshold related to the epsilon above?
mds_best_r6

mds_secbest_r6<-postMDS(mds_r6[[ordered_r6[2]]],
                        geodist_r6, 
                        pc = TRUE, 
                        halfchange = TRUE, 
                        threshold = ep)
mds_secbest_r6

## Procrustes comparisons
procr_r6 <- procrustes(mds_best_r6,
                       mds_secbest_r6,
                       permutations=999)
protest(mds_best_r6,
        mds_secbest_r6,
        permutations=999)

plot(procr_r6)

png(file.path(dataPath,"outputs/BarentsDeep_procrustes_r6.png"), width=1000, height=700,) #added 1000
plot(procr_r6)
dev.off()
```

 #### 1000 reps - don't run if loading saved object 
 Currently commented out as it gained nothing but took extra time. Can be removed in due course.
<!-- took 1hr -->
<!-- ```{r} -->
<!-- # monoMDS -->
<!-- d <- 2 -->
<!-- mds_r6_1000 <- list()#obj added_1000 -->

<!-- Sys.time() -->
<!-- for (i in 1:1000) {#change from 200 (200 ra in 20min, 1000 ran in 1hr) -->
<!--   mds_r6_1000[[i]]<-monoMDS(geodist_r6,#obj added _1000 -->
<!--                     matrix(c(runif(dim(otu_6)[1]*d)), -->
<!--                            nrow = dim(otu_6)[1]), -->
<!--                     k = d, -->
<!--                     model = "global", -->
<!--                     maxit = 2000, -->
<!--                     smin = 1e-7, -->
<!--                     sfgrmin = 1e-7) -->
<!-- } -->
<!-- Sys.time() -->
<!-- ``` -->

<!-- ##### Save the result - don't run if loading saved object -->
<!-- ```{r} -->
<!-- saveRDS(mds_r6_1000, #obj added_1000 -->
<!--         file = file.path(dataPath,"inputs/mds_r6_1000rep.rds")) #change from 200 -->
<!-- ``` -->

<!-- ##### Best nmds solution r6 1000 rep -->
<!-- ```{r, eval=TRUE} -->
<!-- # Loading geodist object -->
<!-- # geodist_nfi <- readRDS(file = "../SplitRev2_geodist_nfi.rds") -->

<!-- # Loading mds results -->
<!-- # mds <- readRDS(file = "../SplitRev2_mds.rds") -->

<!-- ## Extracting the stress of each nmds iteration -->
<!-- mds_stress_r6_1000<-unlist(lapply(mds_r6_1000, function(v){v[[22]]})) #adeed 1000 -->

<!-- ordered_r6_1000 <-order(mds_stress_r6_1000) -->

<!-- ## Best, second best, and worst solution -->
<!-- mds_stress_r6_1000[ordered_r6_1000[1]] -->
<!-- mds_stress_r6_1000[ordered_r6_1000[2]] -->
<!-- mds_stress_r6_1000[ordered_r6_1000[100]] -->

<!-- ## Scaling of axes to half change units and varimax rotation by postMDS -->
<!-- mds_best_r6_1000<-postMDS(mds_r6_1000[[ordered_r6_1000[1]]], -->
<!--                      geodist_r6,  -->
<!--                      pc = TRUE,  -->
<!--                      halfchange = TRUE,  -->
<!--                      threshold = ep)     # Is this threshold related to the epsilon above? -->
<!-- mds_best_r6_1000 -->

<!-- mds_secbest_r6_1000<-postMDS(mds_r6_1000[[ordered_r6_1000[2]]], -->
<!--                         geodist_r6,  -->
<!--                         pc = TRUE,  -->
<!--                         halfchange = TRUE,  -->
<!--                         threshold = ep) -->
<!-- mds_secbest_r6_1000 -->

<!-- ## Procrustes comparisons -->
<!-- procr_r6_1000 <- procrustes(mds_best_r6_1000, -->
<!--                        mds_secbest_r6_1000, -->
<!--                        permutations=999) -->
<!-- protest(mds_best_r6_1000, -->
<!--         mds_secbest_r6_1000, -->
<!--         permutations=999) -->

<!-- plot(procr_r6_1000) -->

<!-- png(file.path(dataPath,"outputs/procrustes_r6_1000.png"), width=1000, height=700,) #added 1000 -->
<!-- plot(procr_r6_1000) -->
<!-- dev.off() -->
<!-- ``` -->



##### Correlation of axis: DCA vs NMDS 
retain the 200 rep version
```{r}
# Extracting ordination axis
ax <- 2
axis_r6 <- cbind(mds_best_r6$points,
                 scores(dca_r6,
                        display = "sites",
                        origin = TRUE)[, 1:ax])

ggcorr(axis_r6, 
       method=c("everything","kendall"), 
       label = TRUE,
       label_size = 3, 
       label_color = "black",  
       nbreaks = 8,
       label_round = 3,
       low = "red",
       mid = "white",
       high = "green")

```

##### Save the figure
```{r}
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_correlationPCAvsNMDS_r6.png"),
       device = "png",
       dpi=300 )

# Switching direction of NMDS1
# mds_best$points[, 1] <- -mds_best$points[, 1]
```

### Plotting DCA & GNMDS
```{r}
## Adding scores to data frame
otu_6$gnmds1 <- mds_best_r6$points[, 1]
otu_6$gnmds2 <- mds_best_r6$points[, 2]
otu_6$dca1 <- scores(dca_r6, display = "sites", origin = TRUE)[, 1]
otu_6$dca2 <- scores(dca_r6, display = "sites", origin = TRUE)[, 2]

p_gnmds_r6 <- ggplot(data = otu_6,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS",
          subtitle = "First run") +
  geom_point(colour = "red") +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")

p_dca_r6 <- ggplot(data = otu_6,
                   aes(x = -dca1,
                       y = dca2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("DCA",
          subtitle = "First run") +
  geom_point(colour = "red") +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")

p_gnmds_r6 + p_dca_r6


```

##### Save the figure
```{r}
ggsave(file.path(dataPath,"outputs/BarentsDeep_gnmds_dca_r6.png"),
       device = "png", 
       dpi=300)
```


## Species-environment relationships
### Selecting ordination
```{r}
ord <- mds_best_r6

## Axis scores if selected ord is GNMDS
axis <- ord$points %>% as.data.frame

## Axis scores if selected ord is DCA
# axis <- scores(ord,
#                display = "sites",
#                origin = TRUE)[, 1:ax])

```

### Create additional variables
decided to make MLD-bathy vars
```{r}
env<-env %>% 
  mutate ("MLDmean_bathy"=MLDmean_Robinson-(bathy*-1),
          "MLDmin_bathy"=MLDmin_Robinson-(bathy*-1),
          "MLDmax_bathy"=MLDmax_Robinson-(bathy*-1))

env$MLDmean_bathy<-cut(env$MLDmean_bathy, 
      breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
      labels=c('belowMLD','onPycno','inMixLayer'))
env$MLDmin_bathy<-cut(env$MLDmin_bathy, 
      breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
      labels=c('belowMLD','onPycno','inMixLayer'))
env$MLDmax_bathy<-cut(env$MLDmax_bathy, 
      breaks=c(-2560, -20,20,130),#checked range of values first (min -2554, max 123)
      labels=c('belowMLD','onPycno','inMixLayer'))

env$swDensRob_avs<-swRho(salinity=env$Smean_Robinson,
                         temperature=env$Tmean_Robinson,
                         pressure=(env$bathy*-1),
                         eos="unesco")

```



### Correlation ordination axes and environmental variables
#### Removing non-env vars
```{r}
env_cont<-env%>% select(-c(landscape,sedclass,gmorph, MLDmean_bathy, MLDmax_bathy, MLDmin_bathy, #categorical
                           optional, #not a var
                           MLDmax_Robinson, MLDmean_Robinson, MLDmin_Robinson,  #replaced by new vars
                           MLDsd_Robinson, #not meaningful
                           SampID))
env_cont<-env_cont%>% mutate_if(is.integer,as.numeric)
env_corr <- env_cont #%>% select(-c(SampID))

# env_corr$coords.x1<-as.numeric(env_corr$coords.x1)
# env_corr$coords.x2<-as.numeric(env_corr$coords.x2)

env_corr[(!is.numeric(env_corr)),]
```

#### Correlations
```{r}
# Vector to hold correlations
cor_ax1 <- NULL
cor_ax2 <- NULL
pv_ax1 <- NULL
pv_ax2 <- NULL

# NMDS1
for( i in seq(length(env_corr))) {
  ct.i <- cor.test(axis$MDS1,
                   env_corr[, i],
                   method = "kendall")
  cor_ax1[i] <- ct.i$estimate
  pv_ax1[i] <- ct.i$p.value
}

# NMDS2
for( i in seq(length(env_corr))) {
  ct.i <- cor.test(axis$MDS2,
                   env_corr[, i],
                   method = "kendall")
  cor_ax2[i] <- ct.i$estimate
  pv_ax2[i] <- ct.i$p.value
}

cor_tab <- data.frame(env = names(env_corr),
                      ord_ax1 = cor_ax1,
                      pval_ax1 = pv_ax1,
                      ord_ax2 = cor_ax2,
                      pval_ax2 = pv_ax2)

cor_tab

write.csv(x = cor_tab,
          file = file.path(dataPath,"inputs/BarentsDeep_cor-table_r6_200rep_MLD-bathy.csv"),
          row.names = FALSE)
```
### Dot chart to check for gaps in correlation

```{r}
cor_a1_sort<-cor_tab%>%
  mutate(abs_ord_ax1=abs(ord_ax1),
         abs_ord_ax2=abs(ord_ax2)) %>%
  arrange(desc(abs_ord_ax1))

cor_a2_sort<-cor_tab%>%
  mutate(abs_ord_ax1=abs(ord_ax1),
         abs_ord_ax2=abs(ord_ax2)) %>%
  arrange(desc(abs_ord_ax2))

dotchart(cor_a1_sort$abs_ord_ax1, main="Absolute (+/-) correlations between envVars and gnmds axis 1")

cor_cut<-0.49 #decide

cor_sel<-subset(cor_a1_sort,abs_ord_ax1>cor_cut)
cor_sel

as.data.frame(cor_sel$env)
```


### Sel env var (top corr)
```{r}
env_os <- env[, cor_sel$env]
env_os
str(env_os)


```


### Ordisurfs top corr
```{r}
ordsrfs <- list(length = ncol(env_os))

for (i in seq(ncol(env_os))) {
  os.i <- gg_ordisurf(ord = ord,
                      env.var = env_os[, i],
                      pt.size = 1,
                      # binwidth = 0.05,
                      var.label = names(env_os)[i],
                      gen.text.size = 10,
                      title.text.size = 15,
                      leg.text.size = 10)
  
  ordsrfs[[i]] <- os.i$plot
}

ordsrfs_plt <- ggarrange(plotlist = ordsrfs,
                         nrow = 5,
                         ncol = 6)

ordsrfs_plt
```
##### Save some outputs
```{r}
ggexport(ordsrfs_plt,
          filename = file.path(dataPath,"outputs/BarentsDeep_ordisurfs_top_corr.png"),
          width = 2500,
          height = 2000)

```


### Sel env var (manual)
```{r}
env_os_m <- env[,c("Tmean_Robinson", #top corr
                  "salt_max", #top corr
                  "Smax_Robinson", #comparison to top corr
                  "swDensRob_avs", #top corr
                  "BO22_icecoverltmax_ss",#top corr ax2
                  "BO22_icecovermean_ss",#top corr
                  "BO22_dissoxmean_bdmean",#top corr
                  #"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
                  "BO22_ppltmin_ss", #top corr
                  "X.y", #comparison to Y
                  "Y", #top corr
                  "spd_std", #top corr ax2 (blended model)
                  "CSpdsd_Robinson", #comparison to top corr ax2 (blended model)
                  "mud", #highest sed var ax1 + corr
                  "gravel",#highest sed var ax1 - corr
                  "BO22_silicateltmax_bdmean", #just under top corr ax1
                  "bathy" #intuitive for comparisons
                )]
env_os_m
str(env_os_m)

```


### Ordisurfs manually selected
```{r}
ordsrfs_m <- list(length = ncol(env_os_m))

for (i in seq(ncol(env_os_m))) {
  os.i_m <- gg_ordisurf(ord = ord,
                      env.var = env_os_m[, i],
                      pt.size = 1,
                      # binwidth = 0.05,
                      var.label = names(env_os_m)[i],
                      gen.text.size = 10,
                      title.text.size = 15,
                      leg.text.size = 10)
  
  ordsrfs_m[[i]] <- os.i_m$plot
}

ordsrfs_plt_m <- ggarrange(plotlist = ordsrfs_m,
                         nrow = 4,
                         ncol = 4)

ordsrfs_plt_m
```

##### Save some outputs
```{r}
ggexport(ordsrfs_plt_m,
          filename = file.path(dataPath,"outputs/BarentsDeep_ordisurfs_man_sel_domean.png"),
          width = 2000,
          height = 2000)

```




### Envfit
```{r}
## Select if any var should be excluded from envfit (makes less busy to read)
env_os_m_envfit<-env_os_m [,c("Tmean_Robinson", #top corr
                  "salt_max", #top corr
                  "Smax_Robinson", #comparison to top corr
                  "swDensRob_avs", #top corr
                  "BO22_icecoverltmax_ss",#top corr ax2
                  #"BO22_icecovermean_ss",#top corr
                  "BO22_dissoxmean_bdmean",#top corr
                  #"BO22_dissoxltmin_bdmean",#top corr
                  #"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
                  "BO22_ppltmin_ss", #top corr
                  "X.y", #comparison to Y
                  "Y", #top corr
                  "spd_std", #top corr ax2 (blended model)
                 # "CSpdsd_Robinson", #comparison to top corr ax2 (blended model)
                  "mud", #highest sed var ax1 + corr
                  "gravel",#highest sed var ax1 - corr
                  "BO22_silicateltmax_bdmean", #just under top corr ax1
                  "bathy" #intuitive for comparisons
                
  
)]

colnames(env_os_m_envfit)<-c("T", #top corr
                  "sMx", #top corr
                  "SmaxR", #comparison to top corr
                  "swDensR", #top corr
                  "icecovmax",#top corr ax2
                  #"icecovav",#top corr
                  "dissoxav",#top corr
                  #"dissoxmin",#top corr
                  #"BO22_carbonphytoltmin_bdmean",#top corr - no clear gradient in ordisurf
                  "ppltmin", #top corr
                  "X", #comparison to Y
                  "Y", #top corr
                  "spd_std", #top corr ax2 (blended model)
                 # "CSsd", #comparison to top corr ax2 (blended model)
                  "mud", #highest sed var ax1 + corr
                  "gravel",#highest sed var ax1 - corr
                  "SiLtmax", #just under top corr ax1
                  "bathy" #intuitive for comparisons
                 )

## Envfot plot
gg_envfit(ord = ord,
          env = env_os_m_envfit,
          pt.size = 1)

## Envfit analysis

ef <- envfit(ord = ord,
             env = env_os_m_envfit,
             # na.rm = TRUE
             )
efDF <- as.data.frame(scores(ef,
                             display = "vectors"))
```

##### Save the plot
```{r}
ggsave(filename = file.path(dataPath,"outputs/BarentsDeep_EnvFit_man_sel_cln_domean.png"),
       device = "png",
       dpi=300 )
```
### Categorical envVar visualised on the mdsplots

#### use dataset with categorical var included

```{r}
env_vis<-env
env_vis$gnmds1 <- otu_6$gnmds1
env_vis$gnmds2 <- otu_6$gnmds2

env_vis$dca1 <- otu_6$dca1
env_vis$dca2 <- otu_6$dca2
```

#### gnmds w mld mean - bathy
```{r}
dca_int <- ggplot(data = env_vis,
                     aes(x = dca1,
                         y = dca2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("Interactive DCA sample ID plot",
          subtitle = "First run") +
  geom_point(aes(colour = factor(SampID))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")

ggplotly(dca_int)
```





#### gnmds w mld mean - bathy
```{r}
p_mld <- ggplot(data = env_vis,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by proximity to mixed layer depth",
          subtitle = "First run") +
  geom_point(aes(colour = MLDmean_bathy)) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")

p_mld
```

#### gnmds w sedclass
##### deal with sedclass codes
```{r}
env_vis<- env_vis %>% 
  mutate(
    sedclassName = case_when(
            sedclass == "1" ~ "SedCoverR",
            sedclass == "5" ~ "Rock",
            sedclass == "20" ~ "Mud",
            sedclass == "21" ~ "MwBlock",
            sedclass == "40" ~ "sMud",
            sedclass == "80" ~ "mSand",
            sedclass == "100" ~ "Sand",
            sedclass == "110" ~ "gMud",
            sedclass == "115" ~ "gsMud",
            sedclass == "120" ~ "gmSand",
            sedclass == "130" ~ "gSand",
            sedclass == "150" ~ "MSG",
            sedclass == "160" ~ "sGravel",
            sedclass == "170" ~ "Gravel",
            sedclass == "175" ~ "GravBlock",
            sedclass == "185" ~ "SGBmix",
            sedclass == "205" ~ "S/MwB",
            sedclass == "206" ~ "S/MwG/B",
            sedclass == "215" ~ "SGBalt",
            sedclass == "300" ~ "HardSed",
            sedclass == "500" ~ "Biogenic"
                        
    )
  )
```

###### colour palette to cope with up to 25 categorical colours
```{r}
c25 <- c(
  "dodgerblue2", "#E31A1C", # red
  "green4",
  "#6A3D9A", # purple
  "#FF7F00", # orange
  "black", "gold1",
  "skyblue2", "#FB9A99", # lt pink
  "palegreen2",
  "#CAB2D6", # lt purple
  "#FDBF6F", # lt orange
  "gray70", "khaki2",
  "maroon", "orchid1", "deeppink1", "blue1", "steelblue4",
  "darkturquoise", "green1", "yellow4", "yellow3",
  "darkorange4", "brown"
)
```




```{r}

p_sed <- ggplot(data = env_vis,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sediment class",
          subtitle = "First run") +
  geom_point(aes(colour = factor(sedclassName))) +
   scale_colour_manual(values=c25)+
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

p_sed
```

#### gnmds w lanscape
##### deal with sedclass codes
```{r}
env_vis<- env_vis %>% 
  mutate(
    landscapeName = case_when(
            landscape == "1" ~ "Strandflat",
            landscape == "21" ~ "ContSlope",
            landscape == "22" ~ "Canyon",
            landscape == "31" ~ "Valley",
            landscape == "32" ~ "Fjord",
            landscape == "41" ~ "DeepPlain",
            landscape == "42" ~ "SlopePlain",
            landscape == "43" ~ "ShelfPlain",
            landscape == "431" ~ "shallowValley"
    )
  )
```


```{r}

p_land <- ggplot(data = env_vis,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by landscape class",
          subtitle = "First run") +
  geom_point(aes(colour = factor(landscapeName))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

p_land
```


#### Gnmds w gmorph
```{r}

p_gmo <- ggplot(data = env_vis,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by landscape class",
          subtitle = "First run") +
  geom_point(aes(colour = factor(gmorph))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour=guide_legend(ncol=2))

p_gmo
```


```{r}
cat_var_plots<-p_mld+p_sed+p_land+p_gmo
```
##### Save the plot
```{r}
ggexport(cat_var_plots,
          filename = file.path(dataPath,"outputs/BarentsDeep_gnmds_catvar.png"),
          width = 1000,
          height = 800)
```

#### Sample identification in the mdsplot

```{r}
p_gmo <- ggplot(data = env_vis,
                     aes(x = gnmds1,
                         y = gnmds2)) +
  theme_classic() +
  coord_fixed() +
  ggtitle("GNMDS coloured by sample",
          subtitle = "First run") +
  geom_point(aes(colour = factor(SampID))) +
  geom_vline(xintercept = 0,
             linetype = 2,
             colour = "lightgray") +
  geom_hline(yintercept = 0,
             linetype = 2,
             colour = "lightgray")+
  guides(colour="none", size="none")

ggplotly(p_gmo)
```

